----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 1 Estimates - Non-Whites Only.log
  log type:  text
 opened on:  19 Jan 2016, 08:21:07

. do "C:\Users\cdj19\AppData\Local\Temp\STD01000000.tmp"

. ***** Data: Meritocracy Replication Data - Table 1 (for Table 1 - Non-Whites) *****
. ***** Note: there was a mistake in the reporting of this model. The table         *****
. ***** in the original paper states that the estimated model was a logit, but  ***** 
. ***** the estimates were from a linear probability model. See the erratum for *****
. ***** the relevant correction.                                                                                        *****
. 
. ** Model reported in corrected paper: Logit model w/ random intercept & random slope **
. 
. xtmelogit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0 || fips: income_i, cov(unstruct)   

Refining starting values: 

Iteration 0:   log likelihood = -1207.3089  
Iteration 1:   log likelihood = -1183.6762  (not concave)
Iteration 2:   log likelihood = -1179.8141  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1179.8141  (not concave)
Iteration 1:   log likelihood = -1179.0169  
Iteration 2:   log likelihood = -1178.9152  
Iteration 3:   log likelihood = -1177.1896  
Iteration 4:   log likelihood = -1177.1802  
Iteration 5:   log likelihood = -1177.1802  
Iteration 6:   log likelihood = -1177.1802  
Iteration 7:   log likelihood = -1177.1802  (backed up)
Iteration 8:   log likelihood = -1177.1802  (backed up)
Iteration 9:   log likelihood = -1177.1802  (backed up)
Iteration 10:  log likelihood = -1177.1802  (backed up)

Mixed-effects logistic regression               Number of obs      =      2062
Group variable: fips                            Number of groups   =       698

                                                Obs per group: min =         1
                                                               avg =       3.0
                                                               max =       101

Integration points =   7                        Wald chi2(18)      =    100.98
Log likelihood = -1177.1802                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.5588078   1.128059    -0.50   0.620    -2.769763    1.652147
         income_i |  -1.114613   .9250444    -1.20   0.228    -2.927667    .6984403
ginicntyXincome_i |   .2070289   1.926085     0.11   0.914    -3.568028    3.982085
      income_cnty |   -.001905   .4008527    -0.00   0.996     -.787562    .7837519
       black_cnty |   .0264344     .34023     0.08   0.938     -.640404    .6932729
      perc_bush04 |  -.9315343   .4614287    -2.02   0.044    -1.835918   -.0271506
         pop_cnty |   .0758025    .258151     0.29   0.769    -.4301643    .5817692
           educ_i |  -.5257441   .2110819    -2.49   0.013    -.9394571   -.1120311
            age_i |    .001173   .0032641     0.36   0.719    -.0052244    .0075704
         gender_i |   .1728556   .1061902     1.63   0.104    -.0352733    .3809845
          unemp_i |  -.0238018   .1300518    -0.18   0.855    -.2786987     .231095
          union_i |   .0620016   .1537633     0.40   0.687    -.2393688    .3633721
        partyid_i |  -.6522126   .1794339    -3.63   0.000    -1.003897   -.3005287
           ideo_i |  -.1525349   .2075745    -0.73   0.462    -.5593734    .2543037
         attend_i |  -.1007503   .1721479    -0.59   0.558    -.4381541    .2366534
       survid2006 |    .035729   .1559248     0.23   0.819     -.269878     .341336
       survid2007 |   -.583176   .1593528    -3.66   0.000    -.8955017   -.2708502
       survid2009 |   -.786554   .1495844    -5.26   0.000    -1.079734   -.4933739
            _cons |   1.169224   .6776537     1.73   0.084    -.1589528    2.497401
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Unstructured           |
                sd(income_i) |   1.251093   .6528651      .4498859    3.479181
                   sd(_cons) |   .6963769   .4141305      .2170905    2.233819
        corr(income_i,_cons) |         -1   8.54e-06            -1           1
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(3) =     1.25   Prob > chi2 = 0.7402

Note: LR test is conservative and provided only for reference.

. 
. ** Alternative specifications **
. 
. * Linear probability model w/clustered ses *
. 
. reg meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0, cluster(fips)

Linear regression                                      Number of obs =    2062
                                                       F( 18,   697) =    8.18
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0551
                                                       Root MSE      =   .4418

                                      (Std. Err. adjusted for 698 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
      meritocracy |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.0769222   .2195252    -0.35   0.726    -.5079321    .3540878
         income_i |  -.1758231   .1662102    -1.06   0.290    -.5021558    .1505096
ginicntyXincome_i |  -.0138511   .3443404    -0.04   0.968    -.6899197    .6622176
      income_cnty |  -.0067486   .0800252    -0.08   0.933    -.1638679    .1503707
       black_cnty |   .0086266   .0660161     0.13   0.896    -.1209876    .1382408
      perc_bush04 |  -.1782194   .0891747    -2.00   0.046    -.3533027   -.0031361
         pop_cnty |   .0097809   .0291453     0.34   0.737    -.0474421    .0670039
           educ_i |  -.1021464   .0393832    -2.59   0.010    -.1794702   -.0248225
            age_i |   .0003098   .0006049     0.51   0.609    -.0008779    .0014975
         gender_i |   .0323554   .0191716     1.69   0.092    -.0052857    .0699965
          unemp_i |  -.0051579   .0243414    -0.21   0.832    -.0529492    .0426333
          union_i |   .0115755   .0277773     0.42   0.677    -.0429616    .0661127
        partyid_i |  -.1175652   .0358419    -3.28   0.001    -.1879362   -.0471941
           ideo_i |  -.0298365   .0427213    -0.70   0.485    -.1137143    .0540413
         attend_i |   -.017989   .0347297    -0.52   0.605    -.0861764    .0501984
       survid2006 |   .0101933   .0339512     0.30   0.764    -.0564656    .0768521
       survid2007 |  -.1139023   .0312159    -3.65   0.000    -.1751907   -.0526139
       survid2009 |  -.1500472   .0298561    -5.03   0.000    -.2086658   -.0914285
            _cons |   .6749393   .1377927     4.90   0.000     .4044008    .9454778
-----------------------------------------------------------------------------------

. 
. * Logit model w/ clustered ses *
. 
. logit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0, cluster(fips)

Iteration 0:   log pseudolikelihood = -1236.2402  
Iteration 1:   log pseudolikelihood = -1178.6415  
Iteration 2:   log pseudolikelihood = -1177.8077  
Iteration 3:   log pseudolikelihood = -1177.8071  
Iteration 4:   log pseudolikelihood = -1177.8071  

Logistic regression                               Number of obs   =       2062
                                                  Wald chi2(18)   =     119.17
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1177.8071                 Pseudo R2       =     0.0473

                                      (Std. Err. adjusted for 698 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.4994409   1.064853    -0.47   0.639    -2.586514    1.587632
         income_i |  -.9974439   .8705356    -1.15   0.252    -2.703662    .7087745
ginicntyXincome_i |   .1015908   1.787564     0.06   0.955     -3.40197    3.605152
      income_cnty |  -.0438579   .4253453    -0.10   0.918    -.8775194    .7898036
       black_cnty |    .021323   .3322902     0.06   0.949    -.6299539    .6725998
      perc_bush04 |  -.9209718   .4601199    -2.00   0.045     -1.82279   -.0191533
         pop_cnty |   .0579434    .147478     0.39   0.694    -.2311081     .346995
           educ_i |  -.5293685   .2007927    -2.64   0.008    -.9229149   -.1358221
            age_i |   .0011983   .0030778     0.39   0.697    -.0048341    .0072307
         gender_i |   .1628754   .0993295     1.64   0.101    -.0318068    .3575576
          unemp_i |  -.0199833    .127841    -0.16   0.876     -.270547    .2305805
          union_i |   .0632869   .1415858     0.45   0.655    -.2142163      .34079
        partyid_i |  -.6390328    .192195    -3.32   0.001    -1.015728   -.2623377
           ideo_i |   -.148738   .2103409    -0.71   0.479    -.5609986    .2635225
         attend_i |  -.1038664   .1756023    -0.59   0.554    -.4480407    .2403079
       survid2006 |   .0384411   .1525785     0.25   0.801    -.2606072    .3374893
       survid2007 |  -.5670258   .1505191    -3.77   0.000    -.8620379   -.2720137
       survid2009 |  -.7741342   .1480319    -5.23   0.000    -1.064271   -.4839971
            _cons |   1.121974   .7029553     1.60   0.110    -.2557927    2.499741
-----------------------------------------------------------------------------------

. 
. * Linear probability model w/ random intercept *
. 
. xtmixed meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0 || fips: , 

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1243.4776  
Iteration 1:   log likelihood = -1231.8852  
Iteration 2:   log likelihood = -1231.8684  
Iteration 3:   log likelihood = -1231.8684  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      2062
Group variable: fips                            Number of groups   =       698

                                                Obs per group: min =         1
                                                               avg =       3.0
                                                               max =       101


                                                Wald chi2(18)      =    120.31
Log likelihood = -1231.8684                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.0769222   .2060063    -0.37   0.709    -.4806871    .3268428
         income_i |  -.1758231   .1595787    -1.10   0.271    -.4885916    .1369454
ginicntyXincome_i |  -.0138511   .3291999    -0.04   0.966    -.6590711     .631369
      income_cnty |  -.0067486   .0745794    -0.09   0.928    -.1529216    .1394245
       black_cnty |   .0086266   .0656528     0.13   0.895    -.1200505    .1373037
      perc_bush04 |  -.1782194   .0880171    -2.02   0.043    -.3507297    -.005709
         pop_cnty |   .0097809   .0494222     0.20   0.843    -.0870848    .1066466
           educ_i |  -.1021464   .0403772    -2.53   0.011    -.1812842   -.0230085
            age_i |   .0003098   .0006273     0.49   0.621    -.0009197    .0015393
         gender_i |   .0323554   .0201372     1.61   0.108    -.0071129    .0718236
          unemp_i |  -.0051579   .0244365    -0.21   0.833    -.0530526    .0427367
          union_i |   .0115755   .0296334     0.39   0.696    -.0465049    .0696559
        partyid_i |  -.1175652   .0331815    -3.54   0.000    -.1825996   -.0525307
           ideo_i |  -.0298365   .0403818    -0.74   0.460    -.1089833    .0493104
         attend_i |   -.017989   .0330281    -0.54   0.586    -.0827228    .0467448
       survid2006 |   .0101933   .0317082     0.32   0.748    -.0519536    .0723401
       survid2007 |  -.1139023   .0311441    -3.66   0.000    -.1749437    -.052861
       survid2009 |  -.1500472   .0289032    -5.19   0.000    -.2066964   -.0933979
            _cons |   .6749393   .1258061     5.36   0.000      .428364    .9215146
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   3.97e-12   3.59e-12      6.76e-13    2.33e-11
-----------------------------+------------------------------------------------
                sd(Residual) |   .4397609   .0068479       .426542    .4533894
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) =  1.8e-12 Prob >= chibar2 = 1.0000

. 
. * Logit model w/ random intercept *
. 
. xtmelogit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0 || fips: , 

Refining starting values: 

Iteration 0:   log likelihood = -1200.2127  
Iteration 1:   log likelihood = -1184.6756  
Iteration 2:   log likelihood = -1179.2041  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1179.2041  
Iteration 1:   log likelihood = -1179.0627  
Iteration 2:   log likelihood = -1178.2378  
Iteration 3:   log likelihood = -1177.8142  
Iteration 4:   log likelihood = -1177.8071  
Iteration 5:   log likelihood = -1177.8071  

Mixed-effects logistic regression               Number of obs      =      2062
Group variable: fips                            Number of groups   =       698

                                                Obs per group: min =         1
                                                               avg =       3.0
                                                               max =       101

Integration points =   7                        Wald chi2(18)      =    107.65
Log likelihood = -1177.8071                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.4994422   1.034149    -0.48   0.629    -2.526336    1.527452
         income_i |  -.9974448     .83784    -1.19   0.234    -2.639581    .6446915
ginicntyXincome_i |   .1015927   1.723575     0.06   0.953    -3.276553    3.479738
      income_cnty |  -.0438581   .3914859    -0.11   0.911    -.8111564    .7234402
       black_cnty |   .0213226   .3345415     0.06   0.949    -.6343667    .6770118
      perc_bush04 |  -.9209729   .4542997    -2.03   0.043    -1.811384    -.030562
         pop_cnty |   .0579428   .2526311     0.23   0.819    -.4372051    .5530907
           educ_i |  -.5293686   .2080439    -2.54   0.011    -.9371272     -.12161
            age_i |   .0011983   .0032164     0.37   0.709    -.0051057    .0075023
         gender_i |   .1628754   .1042325     1.56   0.118    -.0414166    .3671674
          unemp_i |  -.0199834   .1279942    -0.16   0.876    -.2708475    .2308806
          union_i |   .0632868   .1520592     0.42   0.677    -.2347439    .3613174
        partyid_i |  -.6390329   .1758244    -3.63   0.000    -.9836424   -.2944233
           ideo_i |  -.1487382   .2045537    -0.73   0.467    -.5496561    .2521797
         attend_i |  -.1038663   .1695775    -0.61   0.540    -.4362322    .2284996
       survid2006 |   .0384407   .1535312     0.25   0.802     -.262475    .3393564
       survid2007 |  -.5670262    .156592    -3.62   0.000    -.8739409   -.2601116
       survid2009 |  -.7741345   .1472548    -5.26   0.000    -1.062749   -.4855204
            _cons |   1.121976   .6458941     1.74   0.082     -.143953    2.387906
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   8.22e-08   .2024537             0           .
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =     0.00 Prob>=chibar2 = 1.0000

. 
. * Linear probability model w/ random intercept & random slope (no convergence w/ unstructured random effects) *
. 
. xtmixed meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==0 || fips: income_i, 

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1266.4879  
Iteration 1:   log likelihood = -1233.5201  
Iteration 2:   log likelihood = -1231.8685  
Iteration 3:   log likelihood = -1231.8684  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      2062
Group variable: fips                            Number of groups   =       698

                                                Obs per group: min =         1
                                                               avg =       3.0
                                                               max =       101


                                                Wald chi2(18)      =    120.31
Log likelihood = -1231.8684                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |  -.0769222   .2060063    -0.37   0.709    -.4806871    .3268428
         income_i |  -.1758231   .1595787    -1.10   0.271    -.4885916    .1369454
ginicntyXincome_i |  -.0138511   .3291999    -0.04   0.966    -.6590711     .631369
      income_cnty |  -.0067486   .0745794    -0.09   0.928    -.1529216    .1394245
       black_cnty |   .0086266   .0656528     0.13   0.895    -.1200505    .1373037
      perc_bush04 |  -.1782194   .0880171    -2.02   0.043    -.3507297    -.005709
         pop_cnty |   .0097809   .0494222     0.20   0.843    -.0870848    .1066466
           educ_i |  -.1021464   .0403772    -2.53   0.011    -.1812842   -.0230085
            age_i |   .0003098   .0006273     0.49   0.621    -.0009197    .0015393
         gender_i |   .0323554   .0201372     1.61   0.108    -.0071129    .0718236
          unemp_i |  -.0051579   .0244365    -0.21   0.833    -.0530526    .0427367
          union_i |   .0115755   .0296334     0.39   0.696    -.0465049    .0696559
        partyid_i |  -.1175652   .0331815    -3.54   0.000    -.1825996   -.0525307
           ideo_i |  -.0298365   .0403818    -0.74   0.460    -.1089833    .0493104
         attend_i |   -.017989   .0330281    -0.54   0.586    -.0827228    .0467448
       survid2006 |   .0101933   .0317082     0.32   0.748    -.0519536    .0723401
       survid2007 |  -.1139023   .0311441    -3.66   0.000    -.1749437    -.052861
       survid2009 |  -.1500472   .0289032    -5.19   0.000    -.2066964   -.0933979
            _cons |   .6749393   .1258061     5.36   0.000      .428364    .9215146
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Independent            |
                sd(income_i) |   2.70e-10          .             .           .
                   sd(_cons) |   4.32e-09          .             .           .
-----------------------------+------------------------------------------------
                sd(Residual) |   .4397609          .             .           .
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(2) =     0.00   Prob > chi2 = 1.0000

Note: LR test is conservative and provided only for reference.

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 1 Estimates - Non-Whites Only.log
  log type:  text
 closed on:  19 Jan 2016, 08:34:14
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